{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# AutoML with Text data - Customize Search Space and HPO"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "This tutorial teaches you how to control the hyperparameter tuning process in `TextPrediction` by specifying:\n",
    "\n",
    "- A custom search space of candidate hyperparameter values to consider.\n",
    "- Which hyperparameter optimization algorithm should be used to actually search through this space."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "np.random.seed(123)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Paraphrase Identification\n",
    "\n",
    "We consider a Paraphrase Identification task for illustration. Given a pair of sentences, the goal is to predict whether or not one sentence is a restatement of the other (a binary classification task). Here we train models on the [Microsoft Research Paraphrase Corpus](https://www.microsoft.com/en-us/download/details.aspx?id=52398) dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loaded data from: https://autogluon-text.s3-accelerate.amazonaws.com/glue/mrpc/train.parquet | Columns = 3 / 3 | Rows = 3668 -> 3668\n",
      "Loaded data from: https://autogluon-text.s3-accelerate.amazonaws.com/glue/mrpc/dev.parquet | Columns = 3 / 3 | Rows = 408 -> 408\n"
     ]
    },
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sentence1</th>\n",
       "      <th>sentence2</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Amrozi accused his brother , whom he called \" ...</td>\n",
       "      <td>Referring to him as only \" the witness \" , Amr...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Yucaipa owned Dominick 's before selling the c...</td>\n",
       "      <td>Yucaipa bought Dominick 's in 1995 for $ 693 m...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>They had published an advertisement on the Int...</td>\n",
       "      <td>On June 10 , the ship 's owners had published ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Around 0335 GMT , Tab shares were up 19 cents ...</td>\n",
       "      <td>Tab shares jumped 20 cents , or 4.6 % , to set...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>The stock rose $ 2.11 , or about 11 percent , ...</td>\n",
       "      <td>PG &amp; E Corp. shares jumped $ 1.63 or 8 percent...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Revenue in the first quarter of the year dropp...</td>\n",
       "      <td>With the scandal hanging over Stewart 's compa...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>The Nasdaq had a weekly gain of 17.27 , or 1.2...</td>\n",
       "      <td>The tech-laced Nasdaq Composite .IXIC rallied ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>The DVD-CCA then appealed to the state Supreme...</td>\n",
       "      <td>The DVD CCA appealed that decision to the U.S....</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>That compared with $ 35.18 million , or 24 cen...</td>\n",
       "      <td>Earnings were affected by a non-recurring $ 8 ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Shares of Genentech , a much larger company wi...</td>\n",
       "      <td>Shares of Xoma fell 16 percent in early trade ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           sentence1  \\\n",
       "0  Amrozi accused his brother , whom he called \" ...   \n",
       "1  Yucaipa owned Dominick 's before selling the c...   \n",
       "2  They had published an advertisement on the Int...   \n",
       "3  Around 0335 GMT , Tab shares were up 19 cents ...   \n",
       "4  The stock rose $ 2.11 , or about 11 percent , ...   \n",
       "5  Revenue in the first quarter of the year dropp...   \n",
       "6  The Nasdaq had a weekly gain of 17.27 , or 1.2...   \n",
       "7  The DVD-CCA then appealed to the state Supreme...   \n",
       "8  That compared with $ 35.18 million , or 24 cen...   \n",
       "9  Shares of Genentech , a much larger company wi...   \n",
       "\n",
       "                                           sentence2  label  \n",
       "0  Referring to him as only \" the witness \" , Amr...      1  \n",
       "1  Yucaipa bought Dominick 's in 1995 for $ 693 m...      0  \n",
       "2  On June 10 , the ship 's owners had published ...      1  \n",
       "3  Tab shares jumped 20 cents , or 4.6 % , to set...      0  \n",
       "4  PG & E Corp. shares jumped $ 1.63 or 8 percent...      1  \n",
       "5  With the scandal hanging over Stewart 's compa...      1  \n",
       "6  The tech-laced Nasdaq Composite .IXIC rallied ...      0  \n",
       "7  The DVD CCA appealed that decision to the U.S....      1  \n",
       "8  Earnings were affected by a non-recurring $ 8 ...      0  \n",
       "9  Shares of Xoma fell 16 percent in early trade ...      0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from autogluon.utils.tabular.utils.loaders import load_pd\n",
    "\n",
    "train_data = load_pd.load('https://autogluon-text.s3-accelerate.amazonaws.com/glue/mrpc/train.parquet')\n",
    "dev_data = load_pd.load('https://autogluon-text.s3-accelerate.amazonaws.com/glue/mrpc/dev.parquet')\n",
    "train_data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Paraphrase:\n",
      "Sentence1:  They had published an advertisement on the Internet on June 10, offering the cargo for sale, he added.\n",
      "Sentence2:  On June 10, the ship's owners had published an advertisement on the Internet, offering the explosives for sale.\n",
      "Label:  1\n",
      "\n",
      "Not Paraphrase:\n",
      "Sentence1: Around 0335 GMT, Tab shares were up 19 cents, or 4.4%, at A $4.56, having earlier set a record high of A $4.57.\n",
      "Sentence2: Tab shares jumped 20 cents, or 4.6%, to set a record closing high at A $4.57.\n",
      "Label: 0\n"
     ]
    }
   ],
   "source": [
    "from autogluon_contrib_nlp.data.tokenizers import MosesTokenizer\n",
    "tokenizer = MosesTokenizer('en')\n",
    "print('Paraphrase:')\n",
    "print('Sentence1: ', tokenizer.decode(train_data['sentence1'][2].split()))\n",
    "print('Sentence2: ', tokenizer.decode(train_data['sentence2'][2].split()))\n",
    "print('Label: ', train_data['label'][2])\n",
    "\n",
    "print('\\nNot Paraphrase:')\n",
    "print('Sentence1:', tokenizer.decode(train_data['sentence1'][3].split()))\n",
    "print('Sentence2:', tokenizer.decode(train_data['sentence2'][3].split()))\n",
    "print('Label:', train_data['label'][3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Perform HPO over a Customized Search Space with Random Search\n",
    "\n",
    "To control which hyperparameter values are considered during `fit()`, we specify the `hyperparameters` argument. Rather than specifying a particular fixed value for a hyperparameter, we can specify a space of values to search over via `ag.space`. We can also specify which HPO algorithm to use for the search via `search_strategy` (a simple [random search](https://www.jmlr.org/papers/volume13/bergstra12a/bergstra12a.pdf) is specified below).\n",
    "\n",
    "In this example, we search for good values of the following [hyperparameters](https://d2l.ai/chapter_natural-language-processing-applications/finetuning-bert.html):\n",
    "\n",
    "- warmup \n",
    "- learning rate \n",
    "- dropout before the first task-specific layer \n",
    "- layer-wise learning rate decay \n",
    "- number of task-specific layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "import autogluon as ag\n",
    "from autogluon import TextPrediction as task\n",
    "\n",
    "hyperparameters = {\n",
    "    'models': {\n",
    "            'BertForTextPredictionBasic': {\n",
    "                'search_space': {\n",
    "                    'model.network.agg_net.num_layers': ag.space.Int(0, 3),\n",
    "                    'model.network.agg_net.data_dropout': ag.space.Categorical(False, True),\n",
    "                    'optimization.num_train_epochs': 4,\n",
    "                    'optimization.warmup_portion': ag.space.Real(0.1, 0.2),\n",
    "                    'optimization.layerwise_lr_decay': ag.space.Real(0.8, 1.0),\n",
    "                    'optimization.lr': ag.space.Real(1E-5, 1E-4)\n",
    "                }\n",
    "            },\n",
    "    },\n",
    "    'hpo_params': {\n",
    "        'scheduler': 'fifo',\n",
    "        'search_strategy': 'random'\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "We can now call `fit()` with hyperparameter-tuning over our custom search space. Below `num_trials` controls the maximal number of different hyperparameter configurations for which AutoGluon will train models (5 models are trained under different hyperparameter configurations in this case). To achieve good performance in your applications, you should use larger values of `num_trials`, which may identify superior hyperparameter values but will require longer runtimes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-08-05 09:56:59,315 - root - INFO - All Logs will be saved to ./ag_mrpc_random_search/ag_text_prediction.log\n",
      "2020-08-05 09:56:59,331 - root - INFO - Train Dataset:\n",
      "2020-08-05 09:56:59,332 - root - INFO - Columns:\n",
      "\n",
      "- Text(\n",
      "   name=\"sentence1\"\n",
      "   #total/missing=3117/0\n",
      "   length, min/avg/max=38/118.15/226\n",
      ")\n",
      "- Text(\n",
      "   name=\"sentence2\"\n",
      "   #total/missing=3117/0\n",
      "   length, min/avg/max=42/118.32/215\n",
      ")\n",
      "- Categorical(\n",
      "   name=\"label\"\n",
      "   #total/missing=3117/0\n",
      "   num_class (total/non_special)=2/2\n",
      "   categories=[0, 1]\n",
      "   freq=[995, 2122]\n",
      ")\n",
      "\n",
      "\n",
      "2020-08-05 09:56:59,332 - root - INFO - Tuning Dataset:\n",
      "2020-08-05 09:56:59,333 - root - INFO - Columns:\n",
      "\n",
      "- Text(\n",
      "   name=\"sentence1\"\n",
      "   #total/missing=551/0\n",
      "   length, min/avg/max=46/120.33/219\n",
      ")\n",
      "- Text(\n",
      "   name=\"sentence2\"\n",
      "   #total/missing=551/0\n",
      "   length, min/avg/max=47/121.29/210\n",
      ")\n",
      "- Categorical(\n",
      "   name=\"label\"\n",
      "   #total/missing=551/0\n",
      "   num_class (total/non_special)=2/2\n",
      "   categories=[0, 1]\n",
      "   freq=[199, 352]\n",
      ")\n",
      "\n",
      "\n",
      "2020-08-05 09:56:59,333 - root - INFO - Label columns=['label'], Problem types=['classification'], Label shapes=[2]\n",
      "2020-08-05 09:56:59,333 - root - INFO - Eval Metric=acc, Stop Metric=acc, Log Metrics=['f1', 'mcc', 'auc', 'acc', 'nll']\n",
      "2020-08-05 09:56:59,334 - root - INFO - NumPy-shape semantics has been activated in your code. This is required for creating and manipulating scalar and zero-size tensors, which were not supported in MXNet before, as in the official NumPy library. Please DO NOT manually deactivate this semantics while using `mxnet.numpy` and `mxnet.numpy_extension` modules.\n",
      "2020-08-05 09:56:59,339 - root - INFO - NumPy array semantics has been activated in your code. This allows you to use operators from MXNet NumPy and NumPy Extension modules as well as MXNet NumPy `ndarray`s.\n"
     ]
    },
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    {
     "name": "stderr",
     "output_type": "stream",
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    {
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     "output_type": "stream",
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      "\n"
     ]
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    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictor_mrpc = task.fit(train_data,\n",
    "                          label='label',\n",
    "                          hyperparameters=hyperparameters,\n",
    "                          num_trials=5,\n",
    "                          time_limits=60 * 8,\n",
    "                          ngpus_per_trial=1,\n",
    "                          seed=123,\n",
    "                          output_directory='./ag_mrpc_random_search')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Config = {'search_space▁model.network.agg_net.data_dropout▁choice': 1, 'search_space▁model.network.agg_net.num_layers': 2, 'search_space▁optimization.layerwise_lr_decay': 0.912355300099337, 'search_space▁optimization.lr': 7.14729072876085e-05, 'search_space▁optimization.warmup_portion': 0.19366799436501417}\n",
      "Total Time = 85.30689072608948s\n",
      "Accuracy = 82.35%\n",
      "F1 = 87.46%\n"
     ]
    }
   ],
   "source": [
    "dev_score = predictor_mrpc.evaluate(dev_data, metrics=['acc', 'f1'])\n",
    "print('Best Config = {}'.format(predictor_mrpc.results['best_config']))\n",
    "print('Total Time = {}s'.format(predictor_mrpc.results['total_time']))\n",
    "print('Accuracy = {:.2f}%'.format(dev_score['acc'] * 100))\n",
    "print('F1 = {:.2f}%'.format(dev_score['f1'] * 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A = \"It is simple to solve NLP problems with AutoGluon.\"\n",
      "B = \"With AutoGluon, it is easy to solve NLP problems.\"\n",
      "Prediction = \"True\"\n",
      "Prob = \"[0.00157321 0.9984268 ]\"\n",
      "\n",
      "A = \"It is simple to solve NLP problems with AutoGluon.\"\n",
      "B = \"AutoGluon gives you a very bad user experience for solving NLP problems.\"\n",
      "Prediction = \"False\"\n",
      "Prob = \"[0.5942185 0.4057815]\"\n"
     ]
    }
   ],
   "source": [
    "sentence1 = 'It is simple to solve NLP problems with AutoGluon.'\n",
    "sentence2 = 'With AutoGluon, it is easy to solve NLP problems.'\n",
    "sentence3 = 'AutoGluon gives you a very bad user experience for solving NLP problems.'\n",
    "prediction1 = predictor_mrpc.predict({'sentence1': [sentence1], 'sentence2': [sentence2]})\n",
    "prediction1_prob = predictor_mrpc.predict_proba({'sentence1': [sentence1], 'sentence2': [sentence2]})\n",
    "print('A = \"{}\"'.format(sentence1))\n",
    "print('B = \"{}\"'.format(sentence2))\n",
    "print('Prediction = \"{}\"'.format(prediction1[0] == 1))\n",
    "print('Prob = \"{}\"'.format(prediction1_prob[0]))\n",
    "print('')\n",
    "prediction2 = predictor_mrpc.predict({'sentence1': [sentence1], 'sentence2': [sentence3]})\n",
    "prediction2_prob = predictor_mrpc.predict_proba({'sentence1': [sentence1], 'sentence2': [sentence3]})\n",
    "print('A = \"{}\"'.format(sentence1))\n",
    "print('B = \"{}\"'.format(sentence3))\n",
    "print('Prediction = \"{}\"'.format(prediction2[0] == 1))\n",
    "print('Prob = \"{}\"'.format(prediction2_prob[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Use Bayesian Optimization\n",
    "\n",
    "Instead of random search, we can perform HPO via [Bayesian Optimization](https://distill.pub/2020/bayesian-optimization/). Here we specify **skopt** as the searcher, which uses a BayesOpt implementation from the [scikit-optimize](https://scikit-optimize.github.io/stable/) library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "hyperparameters['hpo_params'] = {\n",
    "    'scheduler': 'fifo',\n",
    "    'search_strategy': 'skopt'\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-08-05 10:03:29,458 - root - INFO - All Logs will be saved to ./ag_mrpc_custom_space_fifo_skopt/ag_text_prediction.log\n",
      "2020-08-05 10:03:29,473 - root - INFO - Train Dataset:\n",
      "2020-08-05 10:03:29,474 - root - INFO - Columns:\n",
      "\n",
      "- Text(\n",
      "   name=\"sentence1\"\n",
      "   #total/missing=3117/0\n",
      "   length, min/avg/max=38/118.52/226\n",
      ")\n",
      "- Text(\n",
      "   name=\"sentence2\"\n",
      "   #total/missing=3117/0\n",
      "   length, min/avg/max=42/119.18/215\n",
      ")\n",
      "- Categorical(\n",
      "   name=\"label\"\n",
      "   #total/missing=3117/0\n",
      "   num_class (total/non_special)=2/2\n",
      "   categories=[0, 1]\n",
      "   freq=[1018, 2099]\n",
      ")\n",
      "\n",
      "\n",
      "2020-08-05 10:03:29,474 - root - INFO - Tuning Dataset:\n",
      "2020-08-05 10:03:29,475 - root - INFO - Columns:\n",
      "\n",
      "- Text(\n",
      "   name=\"sentence1\"\n",
      "   #total/missing=551/0\n",
      "   length, min/avg/max=38/118.25/204\n",
      ")\n",
      "- Text(\n",
      "   name=\"sentence2\"\n",
      "   #total/missing=551/0\n",
      "   length, min/avg/max=46/116.42/208\n",
      ")\n",
      "- Categorical(\n",
      "   name=\"label\"\n",
      "   #total/missing=551/0\n",
      "   num_class (total/non_special)=2/2\n",
      "   categories=[0, 1]\n",
      "   freq=[176, 375]\n",
      ")\n",
      "\n",
      "\n",
      "2020-08-05 10:03:29,475 - root - INFO - Label columns=['label'], Problem types=['classification'], Label shapes=[2]\n",
      "2020-08-05 10:03:29,475 - root - INFO - Eval Metric=acc, Stop Metric=acc, Log Metrics=['f1', 'mcc', 'auc', 'acc', 'nll']\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "13ad00652ddd426f9065e0ccefe037a7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=5.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 62%|██████▏   | 239/388 [00:44<00:27,  5.39it/s]\n",
      " 64%|██████▍   | 249/388 [00:45<00:16,  8.59it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 82%|████████▏ | 319/388 [00:56<00:12,  5.65it/s]\n",
      " 88%|████████▊ | 340/388 [01:00<00:13,  3.63it/s]\n",
      "100%|██████████| 388/388 [01:08<00:00,  5.65it/s]\n",
      "100%|██████████| 388/388 [01:07<00:00,  5.71it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictor_mrpc_skopt = task.fit(train_data, label='label',\n",
    "                                hyperparameters=hyperparameters,\n",
    "                                time_limits=60 * 8,\n",
    "                                num_trials=5,\n",
    "                                ngpus_per_trial=1, seed=123,\n",
    "                                output_directory='./ag_mrpc_custom_space_fifo_skopt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Config = {'search_space▁model.network.agg_net.data_dropout▁choice': 1, 'search_space▁model.network.agg_net.num_layers': 1, 'search_space▁optimization.layerwise_lr_decay': 0.8395952073216385, 'search_space▁optimization.lr': 9.718865685254969e-05, 'search_space▁optimization.warmup_portion': 0.19598219802160757}\n",
      "Total Time = 125.24377870559692s\n",
      "Accuracy = 83.58%\n",
      "F1 = 88.55%\n"
     ]
    }
   ],
   "source": [
    "dev_score = predictor_mrpc_skopt.evaluate(dev_data, metrics=['acc', 'f1'])\n",
    "print('Best Config = {}'.format(predictor_mrpc_skopt.results['best_config']))\n",
    "print('Total Time = {}s'.format(predictor_mrpc_skopt.results['total_time']))\n",
    "print('Accuracy = {:.2f}%'.format(dev_score['acc'] * 100))\n",
    "print('F1 = {:.2f}%'.format(dev_score['f1'] * 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A = \"It is simple to solve NLP problems with AutoGluon.\"\n",
      "B = \"With AutoGluon, it is easy to solve NLP problems.\"\n",
      "Prediction = \"True\"\n",
      "Prob = \"[0.00233506 0.9976649 ]\"\n",
      "\n",
      "A = \"It is simple to solve NLP problems with AutoGluon.\"\n",
      "B = \"AutoGluon gives you a very bad user experience for solving NLP problems.\"\n",
      "Prediction = \"False\"\n",
      "Prob = \"[0.82516044 0.17483962]\"\n"
     ]
    }
   ],
   "source": [
    "predictions = predictor_mrpc_skopt.predict(dev_data)\n",
    "prediction1 = predictor_mrpc_skopt.predict({'sentence1': [sentence1], 'sentence2': [sentence2]})\n",
    "prediction1_prob = predictor_mrpc_skopt.predict_proba({'sentence1': [sentence1], 'sentence2': [sentence2]})\n",
    "print('A = \"{}\"'.format(sentence1))\n",
    "print('B = \"{}\"'.format(sentence2))\n",
    "print('Prediction = \"{}\"'.format(prediction1[0] == 1))\n",
    "print('Prob = \"{}\"'.format(prediction1_prob[0]))\n",
    "print('')\n",
    "prediction2 = predictor_mrpc_skopt.predict({'sentence1': [sentence1], 'sentence2': [sentence3]})\n",
    "prediction2_prob = predictor_mrpc_skopt.predict_proba({'sentence1': [sentence1], 'sentence2': [sentence3]})\n",
    "print('A = \"{}\"'.format(sentence1))\n",
    "print('B = \"{}\"'.format(sentence3))\n",
    "print('Prediction = \"{}\"'.format(prediction2[0] == 1))\n",
    "print('Prob = \"{}\"'.format(prediction2_prob[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Use Hyperband\n",
    "Alternatively, we can instead use the [Hyperband algorithm](https://arxiv.org/pdf/1603.06560.pdf) for HPO. Hyperband will try multiple hyperparameter configurations simultaneously and will early stop training under poor configurations to free compute resources for exploring new hyperparameter configurations. It may be able to identify good hyperparameter values more quickly than other search strategies in your applications."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "hyperparameters['hpo_params'] = {\n",
    "    'scheduler': 'hyperband',\n",
    "    'search_strategy': 'random',\n",
    "    'max_t': 40,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-08-05 10:21:36,402 - root - INFO - All Logs will be saved to ./ag_mrpc_custom_space_hyperband/ag_text_prediction.log\n",
      "2020-08-05 10:21:36,418 - root - INFO - Train Dataset:\n",
      "2020-08-05 10:21:36,419 - root - INFO - Columns:\n",
      "\n",
      "- Text(\n",
      "   name=\"sentence1\"\n",
      "   #total/missing=3117/0\n",
      "   length, min/avg/max=38/118.33/226\n",
      ")\n",
      "- Text(\n",
      "   name=\"sentence2\"\n",
      "   #total/missing=3117/0\n",
      "   length, min/avg/max=42/118.70/215\n",
      ")\n",
      "- Categorical(\n",
      "   name=\"label\"\n",
      "   #total/missing=3117/0\n",
      "   num_class (total/non_special)=2/2\n",
      "   categories=[0, 1]\n",
      "   freq=[1034, 2083]\n",
      ")\n",
      "\n",
      "\n",
      "2020-08-05 10:21:36,419 - root - INFO - Tuning Dataset:\n",
      "2020-08-05 10:21:36,420 - root - INFO - Columns:\n",
      "\n",
      "- Text(\n",
      "   name=\"sentence1\"\n",
      "   #total/missing=551/0\n",
      "   length, min/avg/max=38/119.32/212\n",
      ")\n",
      "- Text(\n",
      "   name=\"sentence2\"\n",
      "   #total/missing=551/0\n",
      "   length, min/avg/max=46/119.17/208\n",
      ")\n",
      "- Categorical(\n",
      "   name=\"label\"\n",
      "   #total/missing=551/0\n",
      "   num_class (total/non_special)=2/2\n",
      "   categories=[0, 1]\n",
      "   freq=[160, 391]\n",
      ")\n",
      "\n",
      "\n",
      "2020-08-05 10:21:36,420 - root - INFO - Label columns=['label'], Problem types=['classification'], Label shapes=[2]\n",
      "2020-08-05 10:21:36,421 - root - INFO - Eval Metric=acc, Stop Metric=acc, Log Metrics=['f1', 'mcc', 'auc', 'acc', 'nll']\n",
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     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictor_mrpc_hyperband = task.fit(train_data, label='label',\n",
    "                                    hyperparameters=hyperparameters,\n",
    "                                    time_limits=60 * 8, ngpus_per_trial=1, seed=123,\n",
    "                                    output_directory='./ag_mrpc_custom_space_hyperband')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Config = {'search_space▁model.network.agg_net.data_dropout▁choice': 1, 'search_space▁model.network.agg_net.num_layers': 2, 'search_space▁optimization.layerwise_lr_decay': 0.912355300099337, 'search_space▁optimization.lr': 7.14729072876085e-05, 'search_space▁optimization.warmup_portion': 0.19366799436501417}\n",
      "Total Time = 336.5965392589569s\n",
      "Accuracy = 83.58%\n",
      "F1 = 88.81%\n"
     ]
    }
   ],
   "source": [
    "dev_score = predictor_mrpc_hyperband.evaluate(dev_data, metrics=['acc', 'f1'])\n",
    "print('Best Config = {}'.format(predictor_mrpc_hyperband.results['best_config']))\n",
    "print('Total Time = {}s'.format(predictor_mrpc_hyperband.results['total_time']))\n",
    "print('Accuracy = {:.2f}%'.format(dev_score['acc'] * 100))\n",
    "print('F1 = {:.2f}%'.format(dev_score['f1'] * 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A = \"It is simple to solve NLP problems with AutoGluon.\"\n",
      "B = \"With AutoGluon, it is easy to solve NLP problems.\"\n",
      "Prediction = \"True\"\n",
      "Prob = \"[0.00141819 0.99858177]\"\n",
      "\n",
      "A = \"It is simple to solve NLP problems with AutoGluon.\"\n",
      "B = \"AutoGluon gives you a very bad user experience for solving NLP problems.\"\n",
      "Prediction = \"False\"\n",
      "Prob = \"[0.7991052  0.20089479]\"\n"
     ]
    }
   ],
   "source": [
    "predictions = predictor_mrpc_hyperband.predict(dev_data)\n",
    "prediction1 = predictor_mrpc_hyperband.predict({'sentence1': [sentence1], 'sentence2': [sentence2]})\n",
    "prediction1_prob = predictor_mrpc_hyperband.predict_proba({'sentence1': [sentence1], 'sentence2': [sentence2]})\n",
    "print('A = \"{}\"'.format(sentence1))\n",
    "print('B = \"{}\"'.format(sentence2))\n",
    "print('Prediction = \"{}\"'.format(prediction1[0] == 1))\n",
    "print('Prob = \"{}\"'.format(prediction1_prob[0]))\n",
    "print('')\n",
    "prediction2 = predictor_mrpc_hyperband.predict({'sentence1': [sentence1], 'sentence2': [sentence3]})\n",
    "prediction2_prob = predictor_mrpc_hyperband.predict_proba({'sentence1': [sentence1], 'sentence2': [sentence3]})\n",
    "print('A = \"{}\"'.format(sentence1))\n",
    "print('B = \"{}\"'.format(sentence3))\n",
    "print('Prediction = \"{}\"'.format(prediction2[0] == 1))\n",
    "print('Prob = \"{}\"'.format(prediction2_prob[0]))"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}